Discrete Event Simulation Environment¶

A discrete event simulation environment can be initialised from a random seed, start-time, tick-size, and step-size (i.e. how long in time each simulated step is)

import bourse

seed = 101
start_time = 0
tick_size = 2
step_size = 100_000

env = bourse.core.StepEnv(seed, start_time, tick_size, step_size)

The state of the simulation is updated in discrete steps, with transactions submitted to a queue to be processed at the end of the step. For example placing new orders

order_id_a = env.place_order(False, 100, 101, price=60)
order_id_b = env.place_order(True, 100, 101, price=70)

To actually update the state of the simulation we call bourse.core.StepEnv.step() which shuffles and processes the queued instructions. Each step also increments time to correctly order transactions.

The simulation environment also tracks market data for each step, for example bid-ask prices can be retrieved using

bid_prices, ask_prices = env.get_prices()

the full level 2 data (price and volumes along with volumes and number of orders at top 10 levels) records can be retrieved with

level_2_data = env.get_market_data()

See bourse.core.StepEnv for full details of the environment API.

bourse.step_sim.run() is a utility for running a simulation from an environment and set of agents. See Simulation Example for a full simulation example.